Chapter -9 : Practice Questions & Materials

Please download the files from the resource section

Problem Statement: This data refers to sales transactions at a store. Each transaction is described by five pieces of information: the date of the sale, the store location, the department within the store where the sale took place, the product that was sold, and the amount of the sale in dollars.

For example, if a customer purchased a Mandalay at a store in New York on January 1st, the information for that sale might be:

Date: January 1st Store: New York Department:Food and beverages Payment: Mode of payment Total: $500.00

With this data, you can analyze various aspects of the store's sales, such as which departments and products are the most popular, or how sales have changed over time. Additionally, you can compare sales across different stores or departments to identify trends and opportunities for improvement.

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The task is to perform the following operations on the dataset:

Load the dataset into a Pandas DataFrame and remove the unnecessary columns (Unnamed columns).

Print the first ten records of the dataset.

Find the most used payment method in each store and their frequency in a column named "Total".

Find the total amount of sales for each department.

Find the average Payment for each store.

Find the highest-selling Department in each store and their frequency in a column named "Total".

Create a bar graph comparing the total sales of each store.

Find the total sales of each department in each store.

Find the average mode of each payment method in each store.

Find the top 3 departments based on the average sales.

Create a bar graph comparing the average sales of the top 3 departments.

Create a pivot table comparing the average of each payment method in each department.

Create a 1-dimensional Numpy array of size 10, filled with zeros.

Generate an array with 10 evenly spaced values between 0 and 1.

Convert a list into a Numpy array and find its shape, size, and dimensions.

Extract all the odd numbers from a given Numpy array and store them in another array.

How to sort a NumPy array along a specific axis?

How do you sort a structured NumPy array by multiple fields?

Given a Numpy array a of shape (m, n), how would you calculate the row-wise mean and column-wise mean of the array?

Given a Numpy array a of shape (m, n), how would you find the element with the maximum absolute value in each row and return the indices of those elements?

File :

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Assignment-Python Data Analysis using Pandas and Numpy.ipynb

537.3 kB

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